April 15, 2024

Quantum computing: What leaders need to know now

Once obscure technologies can manifest in short order, creating a struggle for companies to figure out how to effectively leverage them to gain a competitive advantage.

Quantum computing, an innovation that most cannot define and still do not adequately understand, could be the next dark technology to have a seismic effect on business. Quantum computing applies the laws of quantum mechanics to simulate and solve complex problems that are too difficult for the current genre of classical computers.

In some cases, computers with these quantum capabilities can solve large-scale problems much faster than their classical counterparts. Examples include simulating the behavior of matter, analyzing compounds to create new medicines, optimizing manufacturing plants or global supply chains, and identifying patterns of fraud and risk in financial transactions, among others.

The current field of quantum computers is not yet ready for prime time: McKinsey has estimated that 5,000 quantum computers will be operational by 2030, but that the hardware and software needed to handle the most complex problems will not be available until 2035 or later. . However, organizations need to start thinking now about where they could leverage technology to solve real-world business problems. Some companies already expect to invest more than $15 million a year in quantum computing, according to a November 2022 report.

A group of MIT researchers, in partnership with Accenture, has developed a framework to help technology-savvy executives begin to evaluate the potential of quantum computing for solving problems in their companies.

“Implicitly, there is a race between the classical computer and the quantum computer. For every type of question you want to solve, you want to know what type of computer will win so you can get the most out of it,” Neil Thompson, a research scientist at MIT Sloan and the MIT Computer Science and Artificial Intelligence Laboratory, said at the annual conference. of the MIT 2023 Initiative on the Digital Economy.

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Some companies already expect to invest more than $15 million a year in quantum computing.

Thompson is co-author of “The Quantum Tortoise and the Classical Hare: A Simple Framework for Understanding Which Problems Quantum Computing Will Speed ​​Up (and Which It Won’t),” along with Sukwoong Choi, an assistant professor at the University at Albany. and digital fellow at the MIT Initiative on the Digital Economy, and William S. Moses, assistant professor at the University of Illinois Urbana-Champaign.

“This framework provides a way to analyze the potential impact of switching to quantum computing before making the investment,” Thompson said.

The researchers’ conclusion is that small to moderate-sized problems, the types most common in typical businesses, will not benefit from quantum computing. However, those trying to solve large problems with exponential algorithmic gains and those who need to process very large data sets will gain advantages. “Quantum computing won’t be better for everything, just some things,” Thompson said.

An overview of quantum computing

The idea of ​​building a system that leverages physics principles to simulate problems too difficult to model with traditional digital systems was first proposed in the 1980s. The concept was supported by MIT mathematician Peter Shor, who developed the first known quantum algorithm to break encryption in the 1990s.

Unlike today’s computers and supercomputers that use binary electrical signals to represent ones or zeros, quantum computers employ quantum bits (qubits), which are subatomic particles. When managed properly, qubits can represent combinations of ones and zeros simultaneously. The more qubits, the greater the potential for large-scale computing power for problem solving.

When quantum will be useful

The crux of the framework in “The Quantum Tortoise and the Classical Hare” is the fact that classical computers (the hare) generally operate faster than quantum computers (the tortoise), but require more steps to perform a task than quantum computers (the tortoise). Researchers compare it to following an inefficient path from point A to point B.

With their ability to run more efficient algorithms, quantum computers have the potential to take a more direct path, but because they have slower processing speeds, it could actually take longer to solve the problem. The researchers’ framework aims to help companies determine whether the shortest path or the fastest computer is more valuable, depending on the problem they are trying to solve.

Scientists strive to achieve quantum advantage, which is the ability to use quantum computers to solve problems that are beyond the reach of classical computers. (Some companies are estimated to achieve quantum advantage by 2030.)

But researchers said the focus on quantum advantage overshadows the usefulness of quantum computers as they become cost-competitive with classical computers. To compensate, your framework sets the benchmark of quantum economic advantagewhich occurs when a particular problem can be solved more quickly with a quantum computer than with a classical computer of comparable price.

To determine quantum economic advantage, business and technology leaders will need to consider two conditions:

  • FeasibilityThat is, whether a quantum computer exists that is powerful enough to solve a particular problem.
  • Algorithmic advantagemeaning that a quantum computer would be faster at completing a particular task compared to a classical computer of comparable price.

The overlap between the two is the quantum economic advantage. Thompson advised companies to consider computer speed versus route. “Think of it as a race to get from point A to point B, and the algorithm is the route,” Thompson said. “If the race is short, it may not be worth investing in better route planning. For it to be worth it, it has to be a longer race.”

Other things to consider about the current state of quantum computing

It’s still early, but quantum technology is heating up. Quantum computing is early in its maturity cycle, but the landscape is heating up. IBM launched Osprey, a 433-qubit machine, last year and has set a goal of building a 100,000-qubit machine within 10 years. Google aims to reach one million qubits by the end of the decade. Other players in the nascent space include D-Wave Systems, IonQ, Rigetti Computing, Honeywell, Microsoft, Intel and PsiQuantum, with some of the companies offering cloud quantum computing services. Fortune Business Insights has projected that the quantum computing market will grow from $928.8 million this year to $6.5 billion in 2030, a compound annual growth rate of 32.1%.

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Development, cost and talent challenges persist. Companies are still figuring out how to scale the number of physical qubits that can be integrated into quantum computing systems, as well as optimize how different qubits interact with each other as power increases. Much development work is currently devoted to reducing error rates, or noise, in quantum computing. The technology is also expensive, as the systems require complex cooling technologies to protect the qubits.

The skills gap is another problem: it is difficult to find subject matter experts outside of academic and research circles. McKinsey predicts that by 2025, less than half of quantum jobs will be filled, posing a significant barrier to adoption.

The benefits will occur continuously. Quantum computing becomes more attractive when the quantum algorithm is exponentially faster or significantly better than the classical computing option, or if the size of the problem being addressed is greater than the speed difference between the two. Given the current state of quantum technology, it will first be useful for small-scale problems whose solutions offer lower benefits and only later will it be viable for solving more complex problems that promise greater benefits.

Read next: How to build a technology roadmap in established organizations

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